Method for Detecting and Predicting Performance Trends in Stock Markets

Inactive Publication Date: 2010-12-30
LINDE LEON VAN DER
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Human minds typically have difficulty in quickly processing and making sense of large quantities of numeric and nonnumeric data, particularly in real time.
The task of detecting trends in real time to enable rapid rational decisions is often very difficult.
While there are numerous prior software techniques for handling large volumes of data, such techniques often do not prove useful or meaningful in displaying information in an easy to understand manner to help discern trends to provide a basis for making rational decision to predict likely future outcomes.
For this type of data present mathematical tools have limited functionality in displaying and predicting possible outcomes with reproducible accuracy.
The past history of the series cannot be used to predict the future in any meaningful way.
In sum the theory of random walks in stock market prices presents important challenges to both the chartist and the proponent of fundamental analysis.
The challenge of the theory of random walks to the proponent of fundamental analysis, however, is more involved.

Method used

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  • Method for Detecting and Predicting Performance Trends in Stock Markets
  • Method for Detecting and Predicting Performance Trends in Stock Markets
  • Method for Detecting and Predicting Performance Trends in Stock Markets

Examples

Experimental program
Comparison scheme
Effect test

working example 1

[0055]

It is clear that the top 3 groups, AZ, EX and CN were already identifiedas the top 3 groups by 10.49 AM on Tuesday, November 20, 2006.These 3 groups ended the day with 11.85%; 10.63% and 10.52% increasein performance from opening.Identification at 11.21 of group AZ at 5.32%; and group CN at 3.37%,gave group AZ a net profit of (11.85% - 5.32%) = 6.53% and group CN anet profit of (10.52% - 3.37%) = 7.15 %.Group EX was identified at 10.11 at 3.23% and ended the day with 10.63%,for a net profit of 7.14%.

working example 2

FTSE Mar. 27, 2008

High Positive Performance Analyses

[0056]

T,G and E were already identified as the top 3 groups by 8.11 am.on Thursday 27th of March 2007.Identification at 8:11 of group T at 3.00%; ending at close of market at10.47% with a profit of 7.47%. Group G with a profit of (8.13% - 2.14%) = 5.99% and Group E with a profit of (8.74% - 3.35%) = 5.39%

working example 3

FTSE Apr. 1, 2008

High Negative Performance Analyses

[0057]

T, and J were already identified as the bottom 2 groups by 8:11 am. onMonday 1st of April 2008.Group T (selling short) showed a profit of (11.16% -7.58%) = 3.58%Group J (selling short) showed a profit of (13.80% - 5.84% ) = 7.96%.By adding positive parts of the matrix and comparing it with negative parts ofthe Exeleon Matrix themovement of the entire index can be displayed at an early stage, which allowstimely predictions for profiteering.

[0058]Similar results were obtained in accessing the Nasdaq and Tokyo stock markets.

[0059]With this extension of the Exeleon patent pending algorithm to also operate with Multiple Data Input as a parameter we found that the Exeleon algorithm functions remarkably well to display stock market performance (negative and positive), which allows accurate predictions in real time. The Exeleon algorithm for Multiple Data Input also revealed a “mirror” image of positive performance which operates in c...

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Abstract

A systematic method for detecting trends in Stock Markets' performances based on outcomes generated by a first process, comprising: a) determining a set of possible outcomes associated with a first process; (b) coding the possible outcomes to provide a plurality of separate groups, wherein each possible outcome is systematically allocated to one of the groups; (c) allocating an identifier to each of the groups; (d) monitoring in real time the first process such that actual outcomes generated by the first process are mapped to an identifier in accordance with coding step (b); (e) providing a matrix comprised of a plurality of cells arranged in rows; (f) using an exeleon allocation procedure to allocate each identifier generated in step (d) to said matrix, (for multiple-data-input) and (g) repeating step (f) until a trend of duplicating identifiers becomes self evident.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of priority from U.S. Provisional Patent Application Ser. Nos. 61 / 050,204 (filed May, 3, 2008) and 61 / 175,007 (filed May 2, 2009). The entire content of Provisional Patent Application Ser. Nos. 61 / 050,204 and 61 / 175,007 are incorporated herein by reference.FIELD OF THE INVENTION[0002]This invention relates to detecting and / or predicting possible trends as an aid in stock dealing.BACKGROUND OF THE INVENTION[0003]Human minds typically have difficulty in quickly processing and making sense of large quantities of numeric and nonnumeric data, particularly in real time. The task of detecting trends in real time to enable rapid rational decisions is often very difficult.[0004]While there are numerous prior software techniques for handling large volumes of data, such techniques often do not prove useful or meaningful in displaying information in an easy to understand manner to help discern trends to provide a b...

Claims

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Application Information

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IPC IPC(8): G06Q40/00
CPCG06Q40/06
InventorLINDE, LEON VAN DER
OwnerLINDE LEON VAN DER